Comparing the Predictability of Soft Computing and Statistical Techniques for the Prediction of Tensile Strength of Additively Manufactured Carbon Fiber Polylactic Acid Parts

The objective of this study is to investigate the influence of input factors, namely layer height (LH), print speed (PS), and infill line direction (ID), on the tensile strength (TS) of polymer components fabricated using fused deposition modelling. The primary objective of this study is to construc...

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Veröffentlicht in:	Journal of materials engineering and performance Jg. 33; H. 22; S. 12729 - 12741
Hauptverfasser:	Raj, Abhishek, Tyagi, Bobby, Goyal, Ashish, Sahai, Ankit, Sharma, Rahul Swarup
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2024 Springer Nature B.V
Schlagworte:	Accuracy Adaptive systems Artificial intelligence Artificial neural networks Back propagation networks Carbon fibers Characterization and Evaluation of Materials Chemistry and Materials Science Corrosion and Coatings Engineering Design Fused deposition modeling Fuzzy logic Genetic algorithms Machine learning Materials Science Mechanical properties Neural networks Original Research Article Orthogonal arrays Polylactic acid Polymers Prediction models Quality Control Reliability Response surface methodology Safety and Risk Soft computing Statistical methods Structural integrity Tensile strength Tribology Variables ANN additive manufacturing ANFIS fused deposition modelling RSM carbon fibre PLA
ISSN:	1059-9495, 1544-1024
Online-Zugang:	Volltext
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